PyTorch:记住几层顺序的输出

Question

我在 PyTorch 中有一个模型，并且希望从一个正向传递中提取几层的输出。这可能吗？例如，VGG 前五个卷积层的输出

import torchvision
vgg = torchvision.models.vgg19_bn()

Answer 1

我认为最简单的方法是定义一个新模型，其中定义了所有感兴趣的层：

from torch import nn
import torchvision
class VGG(nn.Module):
def __init__(self):
super(VGG, self).__init__()
vgg = torchvision.models.vgg19_bn()
self.l_00 = list(vgg.features.children())[0]
self.l_01 = list(vgg.features.children())[1]
self.l_02 = list(vgg.features.children())[2]
self.l_03 = list(vgg.features.children())[3]
self.l_04 = list(vgg.features.children())[4]
self.l_05 = list(vgg.features.children())[5]
self.l_06 = list(vgg.features.children())[6]
self.l_07 = list(vgg.features.children())[7]
self.l_08 = list(vgg.features.children())[8]
self.l_09 = list(vgg.features.children())[9]
self.l_10 = list(vgg.features.children())[10]
self.l_11 = list(vgg.features.children())[11]
self.l_12 = list(vgg.features.children())[12]
self.l_13 = list(vgg.features.children())[13]
self.l_14 = list(vgg.features.children())[14]
self.l_15 = list(vgg.features.children())[15]
self.l_16 = list(vgg.features.children())[16]
def forward(self, x):
x  = self.l_00(x)
x  = self.l_01(x)
c1 = self.l_02(x)
x  = self.l_03(x)
x  = self.l_04(x)
c2 = self.l_05(x)
x  = self.l_06(c2)
x  = self.l_07(x)
x  = self.l_08(x)
c3 = self.l_09(x)
x  = self.l_10(c3)
x  = self.l_11(x)
c4 = self.l_12(x)
x  = self.l_13(c4)
x  = self.l_14(x)
x  = self.l_15(x)
c5  = self.l_16(x)
return c1, c2, c3, c4, c5